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latrend (version 1.1.2)

A Framework for Clustering Longitudinal Data

Description

A framework for clustering longitudinal datasets in a standardized way. Provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.

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Install

install.packages('latrend')

Monthly Downloads

421

Version

1.1.2

License

GPL (>= 2)

Issues

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Repository

https://github.com/philips-software/latrend

Maintainer

Niek Den Teuling

Last Published

April 14th, 2021

Functions in latrend (1.1.2)

Convert a list of lcMethod objects to a lcMethods list

as.list.lcMethod

Extract the method arguments as a list

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

Get the cluster names

Convert a list of lcModels to a lcModels list

Update the cluster names

as.data.frame.lcModels

Generate a data.frame containing the argument values per method per row

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

createTestDataFold

Create the test fold data for validation

latrend-specific assertions

$,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

createTrainDataFolds

Create the training data for each of the k models in k-fold cross validation evaluation

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

Get the model estimation time

Check model convergence

evaluate.lcMethod

Substitute the call arguments for their evaluated values

confusionMatrix

Compute the posterior confusion matrix

getExternalMetricNames

Get the names of the available external metrics

dcastRepeatedMeasures

Cast a longitudinal data.frame to a matrix

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

deviance.lcModel

lcModel deviance

getExternalMetricDefinition

Get the external metric definition

formula.lcMethod

Extract formula

Get the unique ids included in this model

interface-custom

custom interface

interface-mixAK

mixAK interface

interface-mclust

mclust interface

interface-dtwclust

dtwclust interface

getInternalMetricDefinition

Get the internal metric definition

createTestDataFolds

Create all k test folds from the training data

interface-flexmix

flexmix interface

Cluster longitudinal data over k folds

clusterTrajectories

Extract the cluster trajectories

Extract lcModel fitted values

Coefficients of a lcModel

Synthetic longitudinal dataset comprising three classes

externalMetric,lcModel,lcModel-method

Compute external model metric(s)

clusterProportions

Proportional size of each cluster

Number of strata per cluster

getName,lcMethodLcmmGMM-method

getInternalMetricNames

Get the names of the available internal metrics

formula.lcModel

Extract the formula of a lcModel

lcMethodFeature

Feature-based clustering

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

defineExternalMetric

Define an external metric for lcModels

defineInternalMetric

Define an internal metric for lcModels

interface-akmedoids

akmedoids interface

Create a lcMethod object of the specified type and arguments

Create a lcMethod object from a call

Two-step clustering through linear mixed modeling and k-means

Specify a custom method based on a model function

Cluster longitudinal data repeatedly

Check whether the argument of a lcMethod has a defined value.

interface-crimCV

crimCV interface

Check if object is of Class

lcMethodMclustLLPA

Longitudinal latent profile analysis

interface-longclust

longclust interface

lcMethodMixTVEM

Specify a MixTVEM

latrend-parallel

Parallel computing using latrend

Cluster longitudinal data

model.frame.lcModel

Extract model training data

interface-featureBased

featureBased interface

Generate a list of lcMethod objects

lcMethodStratify

Specify a stratification method

lcMethodDtwclust

Specify time series clustering via dtwclust

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

latrend-generics

Method- and model-specific generics defined by the latrend package

predictAssignments

Predict the cluster assignments for new trajectories

Get the method specification of a lcModel

Extract the model training data

lcApproxModel-class

lcApproxModel class

generateLongData

Generate longitudinal test data

interface-funFEM

funFEM interface

latrend-package

latrend: A Framework for Clustering Longitudinal Data

Cluster longitudinal data for a list of model specifications

Extract the trajectory identifier variable

predictForCluster

lcModel prediction for a specific cluster

plotClusterTrajectories

Plot cluster trajectories

Specify a zero-inflated repeated-measures GBTM method

Cluster longitudinal data using bootstrapping

lcMethodLcmmGMM

Specify GMM method using lcmm

Extract the log-likelihood of a lcModel

Construct a flat (named) list of lcModel objects

Two-step clustering through linear regression modeling and k-means

lcMethodLcmmGBTM

Specify GBTM method

Specify a longitudinal k-means (KML) method

lcMethodAkmedoids

Specify AKMedoids method

Print lcModels list concisely

getCall.lcModel

Get the model call

interface-mixtools

mixtools interface

Specify a FunFEM method

interface-mixtvem

mixtvem interface

lcMethodFlexmix

Method interface to flexmix()

Select the lcModel with the lowest metric value

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

Argument matching with defaults and parent ellipsis expansion

meltRepeatedMeasures

Convert a repeated measures data matrix to a data.frame

Compute internal model metric(s)

lcMatrixMethod-class

summary.lcModel

Summarize a lcModel

update.lcMethod

Update a method specification

Extract the number of observations from a lcModel

Number of clusters

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

lcModelPartition

Create a lcModel with pre-defined partitioning

plotTrajectories

Plot trajectories

Plot one or more internal metrics for all lcModels

plot,lcModel,ANY-method

responseVariable

Extract the response variable

Specify a model based on a pre-computed result.

lcMethodLongclust

Specify Longclust method

Cluster-handling functions for lcModel implementations.

predictPostprob

lcModel posterior probability prediction

Strip a lcModel for serialization

Update a lcModel

Select the lcModel with the highest metric value

Print the arguments of an lcMethod object

transformFitted

Helper function for ensuring the right fitted() output

trajectoryAssignments

Get the cluster membership of each trajectory

Number of strata

Extract residual standard deviation from a lcModel

transformLatrendData

Transform latrend input data into the right format

model.data.lcModel

Extract the model data that was used for fitting

postProbFromObs

Compute the id-specific postprob matrix from a given observation-level postprob matrix

subset.lcModels

Subsetting a lcModels list based on method arguments

Extract the fitted trajectories for all strata

Extract the time variable

weighted.meanNA

Weighted arithmetic mean ignoring NAs

predict.lcModel

lcModel predictions

Sample an index of a vector weighted by the elements

Specify a random-partitioning method

Sampling times of a lcModel

Posterior probability per fitted id

lcModel-data-filters

Data filters for lcModel

postprobFromAssignments

Create a posterior probability matrix from a vector of cluster assignments.

Quantile-quantile plot

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

residuals.lcModel

Extract lcModel residuals

transformPredict

Helper function that matches the output to the specified newdata